Refactor google_activity_recognition feature: replace "metrics" with "features"

Co-authored-by: Meng Li <AnnieLM1996@gmail.com>
pull/95/head
Mingze Cao 2020-04-08 13:36:36 -05:00
parent 0809001dfa
commit a53ba242f8
4 changed files with 18 additions and 18 deletions

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@ -80,7 +80,7 @@ BLUETOOTH:
GOOGLE_ACTIVITY_RECOGNITION: GOOGLE_ACTIVITY_RECOGNITION:
DAY_SEGMENTS: *day_segments DAY_SEGMENTS: *day_segments
METRICS: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle'] FEATURES: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
BATTERY: BATTERY:
DAY_SEGMENTS: *day_segments DAY_SEGMENTS: *day_segments

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@ -624,7 +624,7 @@ See `Google Activity Recognition Config Code`_
.. - Extract the deltas in Google Activity Recognition dataset: ``expand("data/processed/{pid}/plugin_google_activity_recognition_deltas.csv", pid=config["PIDS"]),`` .. - Extract the deltas in Google Activity Recognition dataset: ``expand("data/processed/{pid}/plugin_google_activity_recognition_deltas.csv", pid=config["PIDS"]),``
- Extract Sensor Metrics: - Extract Sensor Features:
| ``expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],`` | ``expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],``
| ``segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),`` | ``segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),``
@ -643,7 +643,7 @@ See `Google Activity Recognition Config Code`_
- **Script:** ``src/features/google_activity_recognition_deltas.R`` - See the google_activity_recognition_deltas.R_ script. - **Script:** ``src/features/google_activity_recognition_deltas.R`` - See the google_activity_recognition_deltas.R_ script.
- **Rule:** ``rules/features.snakefile/activity_metrics`` - See the activity_metrics_ rule. - **Rule:** ``rules/features.snakefile/activity_features`` - See the activity_features_ rule.
- **Script:** ``ssrc/features/google_activity_recognition.py`` - See the google_activity_recognition.py_ script. - **Script:** ``ssrc/features/google_activity_recognition.py`` - See the google_activity_recognition.py_ script.
@ -655,14 +655,14 @@ See `Google Activity Recognition Config Code`_
Name Description Name Description
============ =================== ============ ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
metrics The different measures that can be retrieved from the Google Activity Recognition dataset. See :ref:`Available Google Activity Recognition Metrics <google-activity-recognition-available-metrics>` Table below features The different measures that can be retrieved from the Google Activity Recognition dataset. See :ref:`Available Google Activity Recognition Features <google-activity-recognition-available-features>` Table below
============ =================== ============ ===================
.. _google-activity-recognition-available-metrics: .. _google-activity-recognition-available-features:
**Available Google Activity Recognition Metrics** **Available Google Activity Recognition Features**
The following table shows a list of the available metrics for the Google Activity Recognition dataset. The following table shows a list of the available features for the Google Activity Recognition dataset.
====================== ============ ============= ====================== ============ =============
Name Units Description Name Units Description

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@ -73,13 +73,13 @@ rule bluetooth_features:
script: script:
"../src/features/bluetooth_features.R" "../src/features/bluetooth_features.R"
rule activity_metrics: rule activity_features:
input: input:
gar_events = "data/raw/{pid}/plugin_google_activity_recognition_with_datetime.csv", gar_events = "data/raw/{pid}/plugin_google_activity_recognition_with_datetime.csv",
gar_deltas = "data/processed/{pid}/plugin_google_activity_recognition_deltas.csv" gar_deltas = "data/processed/{pid}/plugin_google_activity_recognition_deltas.csv"
params: params:
segment = "{day_segment}", segment = "{day_segment}",
metrics = config["GOOGLE_ACTIVITY_RECOGNITION"]["METRICS"] features = config["GOOGLE_ACTIVITY_RECOGNITION"]["FEATURES"]
output: output:
"data/processed/{pid}/google_activity_recognition_{day_segment}.csv" "data/processed/{pid}/google_activity_recognition_{day_segment}.csv"
script: script:

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@ -4,12 +4,12 @@ import scipy.stats as stats
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
day_segment = snakemake.params["segment"] day_segment = snakemake.params["segment"]
metrics = snakemake.params["metrics"] features = snakemake.params["features"]
#Read csv into a pandas dataframe #Read csv into a pandas dataframe
data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time']) data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
columns = list("ar_" + str(day_segment) + "_" + column for column in metrics) columns = list("ar_" + str(day_segment) + "_" + column for column in features)
if data.empty: if data.empty:
finalDataset = pd.DataFrame(columns = columns) finalDataset = pd.DataFrame(columns = columns)
@ -31,30 +31,30 @@ else:
finalDataset = pd.DataFrame(columns = columns) finalDataset = pd.DataFrame(columns = columns)
else: else:
#Finding the count of samples of the day #Finding the count of samples of the day
if("count" in metrics): if("count" in features):
finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count() finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
#Finding most common activity of the day #Finding most common activity of the day
if("mostcommonactivity" in metrics): if("mostcommonactivity" in features):
finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None) finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
#finding different number of activities during a day #finding different number of activities during a day
if("countuniqueactivities" in metrics): if("countuniqueactivities" in features):
finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique() finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
#finding Number of times activity changed #finding Number of times activity changed
if("activitychangecount" in metrics): if("activitychangecount" in features):
resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1)) resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0) resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum() finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
deltas_metrics = {'sumstationary':['still','tilting'], deltas_features = {'sumstationary':['still','tilting'],
'summobile':['on_foot','running','on_bicycle'], 'summobile':['on_foot','running','on_bicycle'],
'sumvehicle':['in_vehicle']} 'sumvehicle':['in_vehicle']}
for column, activity_labels in deltas_metrics.items(): for column, activity_labels in deltas_features.items():
if column in metrics: if column in features:
finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))] finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]
.groupby(['local_start_date'])['time_diff'] .groupby(['local_start_date'])['time_diff']
.agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'})) .agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))